
#importing packages

import numpy as np   
import pandas as pd 
from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_samples, silhouette_score   
import matplotlib.pyplot as plt 
import seaborn as sns 
sns.set(color_codes=True)
%matplotlib inline
from scipy import stats 
from scipy.stats import zscore
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split


df=pd.read_csv("C:\\Users\\91630\\Downloads\\Car name.csv")
df1=pd.read_json ("C:\\Users\\91630\\Downloads\\Car-Attributes.json")
car=pd.concat([df,df1],axis=1)
car.head()


car.shape

(398, 9)


car.describe().transpose()


car.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   car_name  398 non-null    object 
 1   mpg       398 non-null    float64
 2   cyl       398 non-null    int64  
 3   disp      398 non-null    float64
 4   hp        398 non-null    object 
 5   wt        398 non-null    int64  
 6   acc       398 non-null    float64
 7   yr        398 non-null    int64  
 8   origin    398 non-null    int64  
dtypes: float64(3), int64(4), object(2)
memory usage: 28.1+ KB


# '?' in 'horsepower' 
hpIsDigit = pd.DataFrame(car.hp.str.isdigit()) 
car[hpIsDigit['hp'] == False]


# Replace ? with NaN
car = car.replace('?', np.nan)
car[hpIsDigit['hp'] == False]


miss_val=car.isnull().sum()
print(miss_val)

car_name    0
mpg         0
cyl         0
disp        0
hp          6
wt          0
acc         0
yr          0
origin      0
dtype: int64


miss_per=miss_val/398
miss_per

car_name    0.000000
mpg         0.000000
cyl         0.000000
disp        0.000000
hp          0.015075
wt          0.000000
acc         0.000000
yr          0.000000
origin      0.000000
dtype: float64


car['hp'].fillna((car['hp'].median()), inplace=True)
car.isnull().sum()

car_name    0
mpg         0
cyl         0
disp        0
hp          0
wt          0
acc         0
yr          0
origin      0
dtype: int64


car_num=car.drop(['yr','origin','car_name','cyl'],axis=1)
sns.pairplot(car_num, diag_kind='kde')

<seaborn.axisgrid.PairGrid at 0x172d406cca0>


sns.scatterplot(x='wt', y='disp', data=car, hue='cyl')

<AxesSubplot:xlabel='wt', ylabel='disp'>


sns.scatterplot(x='wt', y='mpg', data=car, hue='cyl')

<AxesSubplot:xlabel='wt', ylabel='mpg'>


#for numerical variable
data = car.iloc[:,1:8] 
data.head()


dataz = data.apply(zscore)
dataz.head()


#calculating the within sum of squares
wss =[] 
for i in range(2,10):
    KM = KMeans(n_clusters=i)
    KM.fit(dataz)
    wss.append(KM.inertia_)
wss

[1294.8418950727319,
 946.0197908553793,
 738.3923281527312,
 664.8890032028568,
 595.1012606017634,
 541.6206209031756,
 497.07465607265,
 453.93645620194513]


plt.plot(range(2,10), wss);
plt.xlabel("Number of Clusters")
plt.ylabel("WSS");


#using 4 centroids for clustering
k_means = KMeans(n_clusters = 4)
k_means.fit(dataz)
labels = k_means.labels_
silhouette_score(dataz,labels)

0.3481793333017884


X = car.drop(['mpg','origin','car_name'], axis=1)
y = car[['mpg']]

# Split X and y into training and test set in 70:30 ratio

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=15)


from sklearn.linear_model import LinearRegression
regmodel = LinearRegression()
regmodel.fit(X_train, y_train)

LinearRegression()

LinearRegression()


regmodel.score(X_train, y_train)

0.8116062907496024


regmodel.score(X_test, y_test)

0.8003852072685539


final_model=KMeans(4)
final_model.fit(dataz)
prediction=final_model.predict(dataz)

#Append the prediction 
data["GROUP"] = prediction
dataz["GROUP"] = prediction
print("Groups Assigned : \n")
data.head()

Groups Assigned :


dataz.boxplot(by='GROUP', layout = (2,4),figsize=(15,10))

array([[<AxesSubplot:title={'center':'acc'}, xlabel='[GROUP]'>,
        <AxesSubplot:title={'center':'cyl'}, xlabel='[GROUP]'>,
        <AxesSubplot:title={'center':'disp'}, xlabel='[GROUP]'>,
        <AxesSubplot:title={'center':'hp'}, xlabel='[GROUP]'>],
       [<AxesSubplot:title={'center':'mpg'}, xlabel='[GROUP]'>,
        <AxesSubplot:title={'center':'wt'}, xlabel='[GROUP]'>,
        <AxesSubplot:title={'center':'yr'}, xlabel='[GROUP]'>,
        <AxesSubplot:>]], dtype=object)


#import the dataset
df = pd.read_csv("C:\\Users\\91630\\Downloads\\vehicle.csv")
df.head()


#checking for the dimension of the data
rows, column = df.shape
print('The dataset contains', rows, 'rows and', column, 'columns.')

The dataset contains 846 rows and 19 columns.


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 846 entries, 0 to 845
Data columns (total 19 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   compactness                  846 non-null    int64  
 1   circularity                  841 non-null    float64
 2   distance_circularity         842 non-null    float64
 3   radius_ratio                 840 non-null    float64
 4   pr.axis_aspect_ratio         844 non-null    float64
 5   max.length_aspect_ratio      846 non-null    int64  
 6   scatter_ratio                845 non-null    float64
 7   elongatedness                845 non-null    float64
 8   pr.axis_rectangularity       843 non-null    float64
 9   max.length_rectangularity    846 non-null    int64  
 10  scaled_variance              843 non-null    float64
 11  scaled_variance.1            844 non-null    float64
 12  scaled_radius_of_gyration    844 non-null    float64
 13  scaled_radius_of_gyration.1  842 non-null    float64
 14  skewness_about               840 non-null    float64
 15  skewness_about.1             845 non-null    float64
 16  skewness_about.2             845 non-null    float64
 17  hollows_ratio                846 non-null    int64  
 18  class                        846 non-null    object 
dtypes: float64(14), int64(4), object(1)
memory usage: 125.7+ KB


df.isnull().sum()

compactness                    0
circularity                    5
distance_circularity           4
radius_ratio                   6
pr.axis_aspect_ratio           2
max.length_aspect_ratio        0
scatter_ratio                  1
elongatedness                  1
pr.axis_rectangularity         3
max.length_rectangularity      0
scaled_variance                3
scaled_variance.1              2
scaled_radius_of_gyration      2
scaled_radius_of_gyration.1    4
skewness_about                 6
skewness_about.1               1
skewness_about.2               1
hollows_ratio                  0
class                          0
dtype: int64


#replacing the missing values with median values

for cols in df.columns:
    if(cols != 'class'): 
        df[cols] = df[cols].fillna(df[cols].median())


df.describe().T


def label_function(val):
    return f'{val / 100 * len(df):.0f}\n{val:.0f}%'

df.groupby('class').size().plot(kind='pie', autopct=label_function)

<AxesSubplot:ylabel='None'>


print('Duplicated rows: ', df[df.duplicated()].shape[0])

Duplicated rows:  0


from sklearn import svm

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=15)

clf = svm.SVC(gamma=0.025, C=3)  
clf.fit(X_train , y_train)

SVC(C=3, gamma=0.025)

SVC(C=3, gamma=0.025)


y_pred = clf.predict(X_test)


#splitting the data to check for correlation with the class variable
X = df.drop(['class'],axis=1)
y = df['class'].astype('category').cat.codes


sns.pairplot(X, diag_kind='kde')

<seaborn.axisgrid.PairGrid at 0x172e9c2f910>


XScaled=X.apply(zscore)
XScaled.head()


covMatrix = np.cov(XScaled,rowvar=False)
print(covMatrix)

[[ 1.00118343  0.68569786  0.79086299  0.69055952  0.09164265  0.14842463
   0.81358214 -0.78968322  0.81465658  0.67694334  0.76297234  0.81497566
   0.58593517 -0.24988794  0.23635777  0.15720044  0.29889034  0.36598446]
 [ 0.68569786  1.00118343  0.79325751  0.6216467   0.15396023  0.25176438
   0.8489411  -0.82244387  0.84439802  0.96245572  0.79724837  0.83693508
   0.92691166  0.05200785  0.14436828 -0.01145212 -0.10455005  0.04640562]
 [ 0.79086299  0.79325751  1.00118343  0.76794246  0.15864319  0.26499957
   0.90614687 -0.9123854   0.89408198  0.77544391  0.86253904  0.88706577
   0.70660663 -0.22621115  0.1140589   0.26586088  0.14627113  0.33312625]
 [ 0.69055952  0.6216467   0.76794246  1.00118343  0.66423242  0.45058426
   0.73529816 -0.79041561  0.70922371  0.56962256  0.79435372  0.71928618
   0.53700678 -0.18061084  0.04877032  0.17394649  0.38266622  0.47186659]
 [ 0.09164265  0.15396023  0.15864319  0.66423242  1.00118343  0.64949139
   0.10385472 -0.18325156  0.07969786  0.1270594   0.27323306  0.08929427
   0.12211524  0.15313091 -0.05843967 -0.0320139   0.24016968  0.26804208]
 [ 0.14842463  0.25176438  0.26499957  0.45058426  0.64949139  1.00118343
   0.16638787 -0.18035326  0.16169312  0.30630475  0.31933428  0.1434227
   0.18996732  0.29608463  0.01561769  0.04347324 -0.02611148  0.14408905]
 [ 0.81358214  0.8489411   0.90614687  0.73529816  0.10385472  0.16638787
   1.00118343 -0.97275069  0.99092181  0.81004084  0.94978498  0.9941867
   0.80082111 -0.02757446  0.07454578  0.21267959  0.00563439  0.1189581 ]
 [-0.78968322 -0.82244387 -0.9123854  -0.79041561 -0.18325156 -0.18035326
  -0.97275069  1.00118343 -0.95011894 -0.77677186 -0.93748998 -0.95494487
  -0.76722075  0.10342428 -0.05266193 -0.18527244 -0.11526213 -0.2171615 ]
 [ 0.81465658  0.84439802  0.89408198  0.70922371  0.07969786  0.16169312
   0.99092181 -0.95011894  1.00118343  0.81189327  0.93533261  0.98938264
   0.79763248 -0.01551372  0.08386628  0.21495454 -0.01867064  0.09940372]
 [ 0.67694334  0.96245572  0.77544391  0.56962256  0.1270594   0.30630475
   0.81004084 -0.77677186  0.81189327  1.00118343  0.74586628  0.79555492
   0.86747579  0.04167099  0.13601231  0.00136727 -0.10407076  0.07686047]
 [ 0.76297234  0.79724837  0.86253904  0.79435372  0.27323306  0.31933428
   0.94978498 -0.93748998  0.93533261  0.74586628  1.00118343  0.94679667
   0.77983844  0.11321163  0.03677248  0.19446837  0.01423606  0.08579656]
 [ 0.81497566  0.83693508  0.88706577  0.71928618  0.08929427  0.1434227
   0.9941867  -0.95494487  0.98938264  0.79555492  0.94679667  1.00118343
   0.79595778 -0.01541878  0.07696823  0.20104818  0.00622636  0.10305714]
 [ 0.58593517  0.92691166  0.70660663  0.53700678  0.12211524  0.18996732
   0.80082111 -0.76722075  0.79763248  0.86747579  0.77983844  0.79595778
   1.00118343  0.19169941  0.16667971 -0.05621953 -0.22471583 -0.11814142]
 [-0.24988794  0.05200785 -0.22621115 -0.18061084  0.15313091  0.29608463
  -0.02757446  0.10342428 -0.01551372  0.04167099  0.11321163 -0.01541878
   0.19169941  1.00118343 -0.08846001 -0.12633227 -0.749751   -0.80307227]
 [ 0.23635777  0.14436828  0.1140589   0.04877032 -0.05843967  0.01561769
   0.07454578 -0.05266193  0.08386628  0.13601231  0.03677248  0.07696823
   0.16667971 -0.08846001  1.00118343 -0.03503155  0.1154338   0.09724079]
 [ 0.15720044 -0.01145212  0.26586088  0.17394649 -0.0320139   0.04347324
   0.21267959 -0.18527244  0.21495454  0.00136727  0.19446837  0.20104818
  -0.05621953 -0.12633227 -0.03503155  1.00118343  0.07740174  0.20523257]
 [ 0.29889034 -0.10455005  0.14627113  0.38266622  0.24016968 -0.02611148
   0.00563439 -0.11526213 -0.01867064 -0.10407076  0.01423606  0.00622636
  -0.22471583 -0.749751    0.1154338   0.07740174  1.00118343  0.89363767]
 [ 0.36598446  0.04640562  0.33312625  0.47186659  0.26804208  0.14408905
   0.1189581  -0.2171615   0.09940372  0.07686047  0.08579656  0.10305714
  -0.11814142 -0.80307227  0.09724079  0.20523257  0.89363767  1.00118343]]


from sklearn.decomposition import PCA
pca = PCA()
X_pca_ = pca.fit_transform(XScaled)


plt.figure(figsize = (12, 8))
plt.plot((np.cumsum(pca.explained_variance_ratio_) * 100), marker = 'X')
plt.xlim(0, 18)
plt.xlabel('Number of Components')
plt.ylabel('Percentage of Cumulative Explained Variance');


print(pca.explained_variance_)

[9.40460261e+00 3.01492206e+00 1.90352502e+00 1.17993747e+00
 9.17260633e-01 5.39992629e-01 3.58870118e-01 2.21932456e-01
 1.60608597e-01 9.18572234e-02 6.64994118e-02 4.66005994e-02
 3.57947189e-02 2.74120657e-02 2.05792871e-02 1.79166314e-02
 1.00257898e-02 2.96445743e-03]


#plotting the line
plt.figure(figsize = (12, 8))
plt.step(list(range(18)), (np.cumsum(pca.explained_variance_ratio_) * 100), where = 'mid')
plt.xlim(0, 18)
plt.xlabel('Number of Components')
plt.ylabel('Percentage of Cumulative Explained Variance')
plt.title('Vehicle Dataset Explained Variance');


#Using 10 components
pca3 = PCA(n_components=10)
pca3.fit(XScaled)
print(pca3.components_)
print(pca3.explained_variance_ratio_)
Xpca3 = pca3.transform(XScaled)

[[ 2.75283688e-01  2.93258469e-01  3.04609128e-01  2.67606877e-01
   8.05039890e-02  9.72756855e-02  3.17092750e-01 -3.14133155e-01
   3.13959064e-01  2.82830900e-01  3.09280359e-01  3.13788457e-01
   2.72047492e-01 -2.08137692e-02  4.14555082e-02  5.82250207e-02
   3.02795063e-02  7.41453913e-02]
 [-1.26953763e-01  1.25576727e-01 -7.29516436e-02 -1.89634378e-01
  -1.22174860e-01  1.07482875e-02  4.81181371e-02  1.27498515e-02
   5.99352482e-02  1.16220532e-01  6.22806229e-02  5.37843596e-02
   2.09233172e-01  4.88525148e-01 -5.50899716e-02 -1.24085090e-01
  -5.40914775e-01 -5.40354258e-01]
 [-1.19922479e-01 -2.48205467e-02 -5.60143254e-02  2.75074211e-01
   6.42012966e-01  5.91801304e-01 -9.76283108e-02  5.76484384e-02
  -1.09512416e-01 -1.70641987e-02  5.63239801e-02 -1.08840729e-01
  -3.14636493e-02  2.86277015e-01 -1.15679354e-01 -7.52828901e-02
   8.73592034e-03  3.95242743e-02]
 [ 7.83843562e-02  1.87337408e-01 -7.12008427e-02 -4.26053415e-02
   3.27257119e-02  3.14147277e-02 -9.57485748e-02  8.22901952e-02
  -9.24582989e-02  1.88005612e-01 -1.19844008e-01 -9.17449325e-02
   2.00095228e-01 -6.55051354e-02  6.04794251e-01 -6.66114117e-01
   1.05526253e-01  4.74890311e-02]
 [ 6.95178336e-02 -8.50649539e-02  4.06645651e-02 -4.61473714e-02
  -4.05494487e-02  2.13432566e-01 -1.54853055e-02  7.68518712e-02
   2.17633157e-03 -6.06366845e-02 -4.56472367e-04 -1.95548315e-02
  -6.15991681e-02  1.45530146e-01  7.29189842e-01  5.99196401e-01
  -1.00602332e-01 -2.98614819e-02]
 [ 1.44875476e-01 -3.02731148e-01 -1.38405773e-01  2.48136636e-01
   2.36932611e-01 -4.19330747e-01  1.16100153e-01 -1.41840112e-01
   9.80561329e-02 -4.61674972e-01  2.36225434e-01  1.57820194e-01
  -1.35576278e-01  2.41356821e-01  2.03209257e-01 -1.91960802e-01
   1.56939174e-01 -2.41222817e-01]
 [ 4.51862331e-01 -2.49103387e-01  7.40350569e-02 -1.76912814e-01
  -3.97876601e-01  5.03413610e-01  6.49879382e-02  1.38112945e-02
   9.66573058e-02 -1.04552173e-01  1.14622578e-01  8.37350220e-02
  -3.73944382e-01  1.11952983e-01 -8.06328902e-02 -2.84558723e-01
   1.81451818e-02  1.57237839e-02]
 [-5.66136785e-01 -1.79851809e-01  4.34748988e-01  1.01998360e-01
  -6.87147927e-02  1.61153097e-01  1.00688056e-01 -2.15497166e-01
   6.35933915e-02 -2.49495867e-01  5.02096319e-02  4.37649907e-02
  -1.08474496e-01 -3.40878491e-01  1.56487670e-01 -2.08774083e-01
  -3.04580219e-01 -3.04186304e-02]
 [-4.84418105e-01 -1.41569001e-02 -1.67572478e-01 -2.30313563e-01
  -2.77128307e-01  1.48032250e-01  5.44574214e-02 -1.56867362e-01
   5.24978759e-03 -6.10362445e-02  2.97588112e-01  8.33669838e-02
   2.41655483e-01  3.20221887e-01  2.21054148e-02  1.01761758e-02
   5.17222779e-01  1.71506343e-01]
 [-2.60076393e-01  9.80779086e-02 -2.05031597e-01 -4.77888949e-02
   1.08075009e-01 -1.18266345e-01  1.65167200e-01 -1.51612333e-01
   1.93777917e-01  4.69059999e-01 -1.29986011e-01  1.58203940e-01
  -6.86493700e-01  1.27648385e-01  9.83643219e-02 -3.55150608e-02
   1.93956186e-02  6.41314778e-02]]
[0.52186034 0.16729768 0.10562639 0.0654746  0.05089869 0.02996413
 0.01991366 0.01231501 0.00891215 0.00509715]


#Using 6 components and printing the eigen vectors
pca3 = PCA(n_components=6)
pca3.fit(XScaled)
print(pca3.components_)
print(pca3.explained_variance_ratio_)
Xpca3 = pca3.transform(XScaled)

[[ 2.75283688e-01  2.93258469e-01  3.04609128e-01  2.67606877e-01
   8.05039890e-02  9.72756855e-02  3.17092750e-01 -3.14133155e-01
   3.13959064e-01  2.82830900e-01  3.09280359e-01  3.13788457e-01
   2.72047492e-01 -2.08137692e-02  4.14555082e-02  5.82250207e-02
   3.02795063e-02  7.41453913e-02]
 [-1.26953763e-01  1.25576727e-01 -7.29516436e-02 -1.89634378e-01
  -1.22174860e-01  1.07482875e-02  4.81181371e-02  1.27498515e-02
   5.99352482e-02  1.16220532e-01  6.22806229e-02  5.37843596e-02
   2.09233172e-01  4.88525148e-01 -5.50899716e-02 -1.24085090e-01
  -5.40914775e-01 -5.40354258e-01]
 [-1.19922479e-01 -2.48205467e-02 -5.60143254e-02  2.75074210e-01
   6.42012966e-01  5.91801304e-01 -9.76283108e-02  5.76484384e-02
  -1.09512416e-01 -1.70641987e-02  5.63239801e-02 -1.08840729e-01
  -3.14636493e-02  2.86277015e-01 -1.15679354e-01 -7.52828901e-02
   8.73592034e-03  3.95242743e-02]
 [ 7.83843562e-02  1.87337408e-01 -7.12008427e-02 -4.26053415e-02
   3.27257119e-02  3.14147277e-02 -9.57485748e-02  8.22901952e-02
  -9.24582989e-02  1.88005612e-01 -1.19844008e-01 -9.17449325e-02
   2.00095228e-01 -6.55051354e-02  6.04794251e-01 -6.66114117e-01
   1.05526253e-01  4.74890311e-02]
 [ 6.95178336e-02 -8.50649539e-02  4.06645651e-02 -4.61473714e-02
  -4.05494487e-02  2.13432566e-01 -1.54853055e-02  7.68518712e-02
   2.17633155e-03 -6.06366845e-02 -4.56472369e-04 -1.95548315e-02
  -6.15991681e-02  1.45530146e-01  7.29189842e-01  5.99196401e-01
  -1.00602332e-01 -2.98614819e-02]
 [ 1.44875476e-01 -3.02731148e-01 -1.38405773e-01  2.48136636e-01
   2.36932611e-01 -4.19330747e-01  1.16100153e-01 -1.41840112e-01
   9.80561333e-02 -4.61674972e-01  2.36225434e-01  1.57820194e-01
  -1.35576278e-01  2.41356821e-01  2.03209257e-01 -1.91960802e-01
   1.56939174e-01 -2.41222817e-01]]
[0.52186034 0.16729768 0.10562639 0.0654746  0.05089869 0.02996413]


#printing the original features and the reduced features
pca_6 = PCA(n_components = 6)
X_pca = pca_6.fit_transform(XScaled)
print('Original number of features:', X.shape[1])
print('Reduced number of features:', X_pca.shape[1])

Original number of features: 18
Reduced number of features: 6


#viewing the first 5 observations of the pca components
pca_df = pd.DataFrame(data = X_pca)
pca_df.head()


#splitting the original data into train and test 70:30
X_train, X_test, y_train, y_test = train_test_split(XScaled, y, test_size = 0.3, random_state = 10)


#splitting the pca data into train and test 70:30
X_tr, X_te, y_tr, y_te = train_test_split(X_pca, y, test_size = 0.3, random_state = 10)


# Building a Support Vector Machine on train data
from sklearn.svm import SVC

svc_model = SVC(C= 4, kernel='rbf', gamma='scale')
svc_model.fit(X_train, y_train)

SVC(C=4)

SVC(C=4)


#predicting on train data
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import confusion_matrix
from sklearn import metrics

sv_train_predict = svc_model .predict(X_train)
print("Model Accuracy on train: {0:.4f}".format(metrics.accuracy_score(y_train, sv_train_predict)))
print()

Model Accuracy on train: 0.9899


#predicting on test data
sv_test_predict = svc_model .predict(X_test)
print("Model Accuracy on test: {0:.4f}".format(metrics.accuracy_score(y_test, sv_test_predict)))
print()

Model Accuracy on test: 0.9764


#visualization of confusion matrix in the form of a heatmap
cm= confusion_matrix(y_test, sv_test_predict)
plt.figure(figsize = (12, 8))
sns.heatmap(cm, annot = True, fmt = 'd')
plt.xlabel('Actual Classes', fontsize = 15)
plt.ylabel('Predicted Classes', fontsize = 15)
plt.title('Confusion Matrix for SVM', fontsize = 15);


#printing classification report
print("Classification Report")
print(metrics.classification_report(y_test, sv_test_predict, labels=[0,1,2]))

Classification Report
              precision    recall  f1-score   support

           0       1.00      0.97      0.99        71
           1       1.00      0.97      0.98       125
           2       0.91      1.00      0.95        58

    accuracy                           0.98       254
   macro avg       0.97      0.98      0.97       254
weighted avg       0.98      0.98      0.98       254


#SVM on the pca data
svc_model_pca = SVC(C= 4, kernel='rbf', gamma='scale')
svc_model_pca.fit(X_tr, y_tr)

SVC(C=4)

SVC(C=4)


#predicting on train data
sv_tr_predict = svc_model_pca .predict(X_tr)
print("Model Accuracy on train: {0:.4f}".format(metrics.accuracy_score(y_tr, sv_tr_predict)))
print()

Model Accuracy on train: 0.9324


#predicting on test data
sv_te_predict = svc_model_pca .predict(X_te)
print("Model Accuracy on test: {0:.4f}".format(metrics.accuracy_score(y_te, sv_te_predict)))
print()

Model Accuracy on test: 0.9134


#visualization of confusion matrix in the form of a heatmap
cm= confusion_matrix(y_te, sv_te_predict)
plt.figure(figsize = (12, 8))
sns.heatmap(cm, annot = True, fmt = 'd')
plt.xlabel('Actual Classes', fontsize = 15)
plt.ylabel('Predicted Classes', fontsize = 15)
plt.title('Confusion Matrix for SVM', fontsize = 15);


#printing classification report
print("Classification Report")
print(metrics.classification_report(y_te, sv_te_predict, labels=[0,1,2]))

Classification Report
              precision    recall  f1-score   support

           0       0.95      0.89      0.92        71
           1       0.92      0.92      0.92       125
           2       0.86      0.93      0.89        58

    accuracy                           0.91       254
   macro avg       0.91      0.91      0.91       254
weighted avg       0.92      0.91      0.91       254

	car_name	mpg	cyl	disp	hp	wt	acc	yr	origin
0	chevrolet chevelle malibu	18.0	8	307.0	130	3504	12.0	70	1
1	buick skylark 320	15.0	8	350.0	165	3693	11.5	70	1
2	plymouth satellite	18.0	8	318.0	150	3436	11.0	70	1
3	amc rebel sst	16.0	8	304.0	150	3433	12.0	70	1
4	ford torino	17.0	8	302.0	140	3449	10.5	70	1

	count	mean	std	min	25%	50%	75%	max
mpg	398.0	23.514573	7.815984	9.0	17.500	23.0	29.000	46.6
cyl	398.0	5.454774	1.701004	3.0	4.000	4.0	8.000	8.0
disp	398.0	193.425879	104.269838	68.0	104.250	148.5	262.000	455.0
wt	398.0	2970.424623	846.841774	1613.0	2223.750	2803.5	3608.000	5140.0
acc	398.0	15.568090	2.757689	8.0	13.825	15.5	17.175	24.8
yr	398.0	76.010050	3.697627	70.0	73.000	76.0	79.000	82.0
origin	398.0	1.572864	0.802055	1.0	1.000	1.0	2.000	3.0

	car_name	mpg	cyl	disp	hp	wt	acc	yr	origin
32	ford pinto	25.0	4	98.0	?	2046	19.0	71	1
126	ford maverick	21.0	6	200.0	?	2875	17.0	74	1
330	renault lecar deluxe	40.9	4	85.0	?	1835	17.3	80	2
336	ford mustang cobra	23.6	4	140.0	?	2905	14.3	80	1
354	renault 18i	34.5	4	100.0	?	2320	15.8	81	2
374	amc concord dl	23.0	4	151.0	?	3035	20.5	82	1

	car_name	mpg	cyl	disp	hp	wt	acc	yr	origin
32	ford pinto	25.0	4	98.0	NaN	2046	19.0	71	1
126	ford maverick	21.0	6	200.0	NaN	2875	17.0	74	1
330	renault lecar deluxe	40.9	4	85.0	NaN	1835	17.3	80	2
336	ford mustang cobra	23.6	4	140.0	NaN	2905	14.3	80	1
354	renault 18i	34.5	4	100.0	NaN	2320	15.8	81	2
374	amc concord dl	23.0	4	151.0	NaN	3035	20.5	82	1

	mpg	cyl	disp	hp	wt	acc	yr
0	18.0	8	307.0	130.0	3504	12.0	70
1	15.0	8	350.0	165.0	3693	11.5	70
2	18.0	8	318.0	150.0	3436	11.0	70
3	16.0	8	304.0	150.0	3433	12.0	70
4	17.0	8	302.0	140.0	3449	10.5	70

Part A¶

Clustering¶

linear regression¶

Kmeans clustering¶

PART B¶

null value¶

Training SVM¶

	mpg	cyl	disp	hp	wt	acc	yr
0	-0.706439	1.498191	1.090604	0.673118	0.630870	-1.295498	-1.627426
1	-1.090751	1.498191	1.503514	1.589958	0.854333	-1.477038	-1.627426
2	-0.706439	1.498191	1.196232	1.197027	0.550470	-1.658577	-1.627426
3	-0.962647	1.498191	1.061796	1.197027	0.546923	-1.295498	-1.627426
4	-0.834543	1.498191	1.042591	0.935072	0.565841	-1.840117	-1.627426

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
0	95	48.0	83.0	178.0	72.0	10	162.0	42.0	20.0	159	176.0	379.0	184.0	70.0	6.0	16.0	187.0	197	van
1	91	41.0	84.0	141.0	57.0	9	149.0	45.0	19.0	143	170.0	330.0	158.0	72.0	9.0	14.0	189.0	199	van
2	104	50.0	106.0	209.0	66.0	10	207.0	32.0	23.0	158	223.0	635.0	220.0	73.0	14.0	9.0	188.0	196	car
3	93	41.0	82.0	159.0	63.0	9	144.0	46.0	19.0	143	160.0	309.0	127.0	63.0	6.0	10.0	199.0	207	van
4	85	44.0	70.0	205.0	103.0	52	149.0	45.0	19.0	144	241.0	325.0	188.0	127.0	9.0	11.0	180.0	183	bus

	count	mean	std	min	25%	50%	75%	max
compactness	846.0	93.678487	8.234474	73.0	87.00	93.0	100.00	119.0
circularity	846.0	44.823877	6.134272	33.0	40.00	44.0	49.00	59.0
distance_circularity	846.0	82.100473	15.741569	40.0	70.00	80.0	98.00	112.0
radius_ratio	846.0	168.874704	33.401356	104.0	141.00	167.0	195.00	333.0
pr.axis_aspect_ratio	846.0	61.677305	7.882188	47.0	57.00	61.0	65.00	138.0
max.length_aspect_ratio	846.0	8.567376	4.601217	2.0	7.00	8.0	10.00	55.0
scatter_ratio	846.0	168.887707	33.197710	112.0	147.00	157.0	198.00	265.0
elongatedness	846.0	40.936170	7.811882	26.0	33.00	43.0	46.00	61.0
pr.axis_rectangularity	846.0	20.580378	2.588558	17.0	19.00	20.0	23.00	29.0
max.length_rectangularity	846.0	147.998818	14.515652	118.0	137.00	146.0	159.00	188.0
scaled_variance	846.0	188.596927	31.360427	130.0	167.00	179.0	217.00	320.0
scaled_variance.1	846.0	439.314421	176.496341	184.0	318.25	363.5	586.75	1018.0
scaled_radius_of_gyration	846.0	174.706856	32.546277	109.0	149.00	173.5	198.00	268.0
scaled_radius_of_gyration.1	846.0	72.443262	7.468734	59.0	67.00	71.5	75.00	135.0
skewness_about	846.0	6.361702	4.903244	0.0	2.00	6.0	9.00	22.0
skewness_about.1	846.0	12.600473	8.930962	0.0	5.00	11.0	19.00	41.0
skewness_about.2	846.0	188.918440	6.152247	176.0	184.00	188.0	193.00	206.0
hollows_ratio	846.0	195.632388	7.438797	181.0	190.25	197.0	201.00	211.0

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
0	0.160580	0.518073	0.057177	0.273363	1.310398	0.311542	-0.207598	0.136262	-0.224342	0.758332	-0.401920	-0.341934	0.285705	-0.327326	-0.073812	0.380870	-0.312012	0.183957
1	-0.325470	-0.623732	0.120741	-0.835032	-0.593753	0.094079	-0.599423	0.520519	-0.610886	-0.344578	-0.593357	-0.619724	-0.513630	-0.059384	0.538390	0.156798	0.013265	0.452977
2	1.254193	0.844303	1.519141	1.202018	0.548738	0.311542	1.148719	-1.144597	0.935290	0.689401	1.097671	1.109379	1.392477	0.074587	1.558727	-0.403383	-0.149374	0.049447
3	-0.082445	-0.623732	-0.006386	-0.295813	0.167907	0.094079	-0.750125	0.648605	-0.610886	-0.344578	-0.912419	-0.738777	-1.466683	-1.265121	-0.073812	-0.291347	1.639649	1.529056
4	-1.054545	-0.134387	-0.769150	1.082192	5.245643	9.444962	-0.599423	0.520519	-0.610886	-0.275646	1.671982	-0.648070	0.408680	7.309005	0.538390	-0.179311	-1.450481	-1.699181

	0	1	2	3	4	5
0	0.334162	-0.219026	1.001584	0.176612	0.079301	-0.757447
1	-1.591711	-0.420603	-0.369034	0.233234	0.693949	-0.517162
2	3.769324	0.195283	0.087859	1.202212	0.731732	0.705041
3	-1.738598	-2.829692	0.109456	0.376685	-0.362897	-0.484431
4	0.558103	4.758422	11.703647	0.147464	3.256953	-0.203446